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Layerweaver plus : A QoS-Aware Layer-Wise DNN Scheduler for Multi-Tenant Neural Processing Units

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Authors

Oh, Young H.; Jin, Yunho; Ham, Tae Jun; Lee, Jae W.

Issue Date
2022-01-01
Publisher
Oxford University Press
Citation
IEICE Transactions on Information and Systems, Vol.E105D No.2, pp.427-431
Abstract
Many cloud service providers employ specialized hardware accelerators, called neural processing units (NPUs), to accelerate deep neural networks (DNNs). An NPU scheduler is responsible for scheduling incoming user requests and required to satisfy the two, often conflicting, optimization goals: maximizing system throughput and satisfying quality-of-service (QoS) constraints (e.g., deadlines) of individual requests. We propose Layerweaver+, a low-cost layer-wise DNN scheduler for NPUs, which provides both high system throughput and minimal QoS violations. For a serving scenario based on the industry-standard MLPerf inference benchmark, Layerweaver+ significantly improves the system throughput by up to 266.7% over the baseline scheduler serving one DNN at a time.
ISSN
0916-8532
URI
https://hdl.handle.net/10371/179351
DOI
https://doi.org/10.1587/transinf.2021EDL8084
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